Overview

Dataset statistics

Number of variables9
Number of observations379021
Missing cells0
Missing cells (%)0.0%
Duplicate rows530
Duplicate rows (%)0.1%
Total size in memory26.0 MiB
Average record size in memory72.0 B

Variable types

Numeric9

Alerts

Dataset has 530 (0.1%) duplicate rowsDuplicates
dropped_frames_mean is highly correlated with dropped_frames_std and 1 other fieldsHigh correlation
dropped_frames_std is highly correlated with dropped_frames_mean and 1 other fieldsHigh correlation
dropped_frames_max is highly correlated with dropped_frames_mean and 1 other fieldsHigh correlation
bitrate_mean is highly correlated with bitrate_stdHigh correlation
bitrate_std is highly correlated with bitrate_meanHigh correlation
rtt_mean is highly correlated with rtt_stdHigh correlation
rtt_std is highly correlated with rtt_meanHigh correlation
fps_mean is highly correlated with fps_stdHigh correlation
fps_std is highly correlated with fps_meanHigh correlation
rtt_mean is highly skewed (γ1 = 28.55656923) Skewed
rtt_std is highly skewed (γ1 = 136.2679109) Skewed
dropped_frames_mean is highly skewed (γ1 = 93.73267892) Skewed
fps_std has 92611 (24.4%) zeros Zeros
rtt_mean has 5496 (1.5%) zeros Zeros
rtt_std has 15520 (4.1%) zeros Zeros
dropped_frames_mean has 368604 (97.3%) zeros Zeros
dropped_frames_std has 368750 (97.3%) zeros Zeros
dropped_frames_max has 368604 (97.3%) zeros Zeros
bitrate_std has 4511 (1.2%) zeros Zeros

Reproduction

Analysis started2022-10-05 00:40:42.167339
Analysis finished2022-10-05 00:41:30.630875
Duration48.46 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

fps_mean
Real number (ℝ≥0)

HIGH CORRELATION

Distinct570
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.23112677
Minimum10
Maximum125.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2022-10-05T03:41:30.831398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile24.1
Q128.8
median30
Q343.6
95-th percentile57.7
Maximum125.8
Range115.8
Interquartile range (IQR)14.8

Descriptive statistics

Standard deviation10.97501034
Coefficient of variation (CV)0.3115145992
Kurtosis-0.3639516981
Mean35.23112677
Median Absolute Deviation (MAD)2.9
Skewness0.9322321979
Sum13353336.9
Variance120.4508519
MonotonicityNot monotonic
2022-10-05T03:41:31.116370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3080492
 
21.2%
29.915270
 
4.0%
29.88850
 
2.3%
29.76579
 
1.7%
30.16014
 
1.6%
29.65423
 
1.4%
29.54660
 
1.2%
254654
 
1.2%
29.43912
 
1.0%
243578
 
0.9%
Other values (560)239589
63.2%
ValueCountFrequency (%)
1029
< 0.1%
10.54
 
< 0.1%
10.65
 
< 0.1%
10.712
 
< 0.1%
10.89
 
< 0.1%
10.915
< 0.1%
1131
< 0.1%
11.123
< 0.1%
11.231
< 0.1%
11.331
< 0.1%
ValueCountFrequency (%)
125.81
< 0.1%
102.71
< 0.1%
98.71
< 0.1%
95.41
< 0.1%
91.81
< 0.1%
83.71
< 0.1%
831
< 0.1%
80.21
< 0.1%
76.31
< 0.1%
75.91
< 0.1%

fps_std
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct17929
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.725705034
Minimum0
Maximum307.1672726
Zeros92611
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2022-10-05T03:41:31.381934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.316227766
median0.9428090416
Q32.233582076
95-th percentile6.307843442
Maximum307.1672726
Range307.1672726
Interquartile range (IQR)1.91735431

Descriptive statistics

Standard deviation2.505942402
Coefficient of variation (CV)1.452126726
Kurtosis701.7718804
Mean1.725705034
Median Absolute Deviation (MAD)0.9428090416
Skewness9.699972994
Sum654078.4477
Variance6.279747322
MonotonicityNot monotonic
2022-10-05T03:41:31.648641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
092611
 
24.4%
0.3162277666311
 
1.7%
0.3162277664891
 
1.3%
0.3162277664085
 
1.1%
0.3162277664053
 
1.1%
0.3162277663086
 
0.8%
0.42163702142058
 
0.5%
0.42163702141975
 
0.5%
0.47140452081963
 
0.5%
0.94868329811935
 
0.5%
Other values (17919)256053
67.6%
ValueCountFrequency (%)
092611
24.4%
0.316227766202
 
0.1%
0.3162277664891
 
1.3%
0.3162277663086
 
0.8%
0.3162277664053
 
1.1%
0.3162277661
 
< 0.1%
0.3162277666311
 
1.7%
0.3162277664085
 
1.1%
0.316227766415
 
0.1%
0.316227766156
 
< 0.1%
ValueCountFrequency (%)
307.16727261
< 0.1%
148.8989591
< 0.1%
141.15637511
< 0.1%
139.65676181
< 0.1%
103.27358491
< 0.1%
95.818172711
< 0.1%
88.872943011
< 0.1%
63.981334081
< 0.1%
62.898507311
< 0.1%
62.229057161
< 0.1%

rtt_mean
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct4769
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.62385778
Minimum0
Maximum12898.4
Zeros5496
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2022-10-05T03:41:31.898585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.5
Q114.3
median32.2
Q355.9
95-th percentile162.3
Maximum12898.4
Range12898.4
Interquartile range (IQR)41.6

Descriptive statistics

Standard deviation94.78109805
Coefficient of variation (CV)1.909990522
Kurtosis1921.808241
Mean49.62385778
Median Absolute Deviation (MAD)19.1
Skewness28.55656923
Sum18808484.2
Variance8983.456547
MonotonicityNot monotonic
2022-10-05T03:41:32.167173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05496
 
1.5%
13.11203
 
0.3%
131157
 
0.3%
13.31151
 
0.3%
13.41143
 
0.3%
13.21139
 
0.3%
51090
 
0.3%
12.91080
 
0.3%
16.11070
 
0.3%
12.61068
 
0.3%
Other values (4759)363424
95.9%
ValueCountFrequency (%)
05496
1.5%
0.21
 
< 0.1%
0.34
 
< 0.1%
0.42
 
< 0.1%
0.53
 
< 0.1%
0.61
 
< 0.1%
0.76
 
< 0.1%
0.84
 
< 0.1%
0.93
 
< 0.1%
110
 
< 0.1%
ValueCountFrequency (%)
12898.41
< 0.1%
9021.41
< 0.1%
7191.81
< 0.1%
7078.71
< 0.1%
6072.81
< 0.1%
58891
< 0.1%
5885.61
< 0.1%
5742.11
< 0.1%
5561.81
< 0.1%
5255.71
< 0.1%

rtt_std
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct57786
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.76367188
Minimum0
Maximum40721.93329
Zeros15520
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2022-10-05T03:41:32.432738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.316227766
Q10.6992058988
median1.433720878
Q34.948624949
95-th percentile33.64867506
Maximum40721.93329
Range40721.93329
Interquartile range (IQR)4.249419051

Descriptive statistics

Standard deviation112.68446
Coefficient of variation (CV)8.828529988
Kurtosis45230.45394
Mean12.76367188
Median Absolute Deviation (MAD)0.9506749863
Skewness136.2679109
Sum4837699.68
Variance12697.78752
MonotonicityNot monotonic
2022-10-05T03:41:32.690659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
015520
 
4.1%
0.48304589153208
 
0.8%
0.51639777952934
 
0.8%
0.42163702142676
 
0.7%
0.3162277662597
 
0.7%
0.51639777952372
 
0.6%
0.56764621222179
 
0.6%
0.3162277662163
 
0.6%
0.67494855772098
 
0.6%
0.69920589881896
 
0.5%
Other values (57776)341378
90.1%
ValueCountFrequency (%)
015520
4.1%
0.3162277661
 
< 0.1%
0.3162277662
 
< 0.1%
0.3162277663
 
< 0.1%
0.3162277668
 
< 0.1%
0.3162277662
 
< 0.1%
0.31622776631
 
< 0.1%
0.31622776638
 
< 0.1%
0.31622776632
 
< 0.1%
0.316227766234
 
0.1%
ValueCountFrequency (%)
40721.933291
< 0.1%
5413.645821
< 0.1%
5215.5416931
< 0.1%
4716.1210531
< 0.1%
4452.1035991
< 0.1%
4363.5266271
< 0.1%
4292.5447571
< 0.1%
4207.6835661
< 0.1%
4153.0915451
< 0.1%
4062.7282041
< 0.1%

dropped_frames_mean
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct298
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1804506874
Minimum0
Maximum540
Zeros368604
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2022-10-05T03:41:32.949632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum540
Range540
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.732890313
Coefficient of variation (CV)9.603123921
Kurtosis25322.36518
Mean0.1804506874
Median Absolute Deviation (MAD)0
Skewness93.73267892
Sum68394.6
Variance3.002908837
MonotonicityNot monotonic
2022-10-05T03:41:33.210053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0368604
97.3%
3.3503
 
0.1%
3.4485
 
0.1%
3.5405
 
0.1%
3.6297
 
0.1%
0.1230
 
0.1%
3.2228
 
0.1%
6.6228
 
0.1%
6.4210
 
0.1%
6.7206
 
0.1%
Other values (288)7625
 
2.0%
ValueCountFrequency (%)
0368604
97.3%
0.1230
 
0.1%
0.210
 
< 0.1%
0.37
 
< 0.1%
0.411
 
< 0.1%
0.52
 
< 0.1%
0.63
 
< 0.1%
0.710
 
< 0.1%
0.82
 
< 0.1%
0.96
 
< 0.1%
ValueCountFrequency (%)
5401
 
< 0.1%
1232
 
< 0.1%
774
 
< 0.1%
761
 
< 0.1%
73.81
 
< 0.1%
722
 
< 0.1%
6811
< 0.1%
64.81
 
< 0.1%
646
< 0.1%
61.51
 
< 0.1%

dropped_frames_std
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1144
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4695475059
Minimum0
Maximum202.3857703
Zeros368750
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2022-10-05T03:41:33.483024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum202.3857703
Range202.3857703
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.157866214
Coefficient of variation (CV)6.725339129
Kurtosis193.9396541
Mean0.4695475059
Median Absolute Deviation (MAD)0
Skewness9.655549724
Sum177968.3652
Variance9.972119027
MonotonicityNot monotonic
2022-10-05T03:41:33.733403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0368750
97.3%
10.43551628501
 
0.1%
11.06797181398
 
0.1%
10.75174404334
 
0.1%
11.38419958298
 
0.1%
0.316227766231
 
0.1%
10.11928851227
 
0.1%
20.23857703204
 
0.1%
11.70042734196
 
0.1%
20.87103256196
 
0.1%
Other values (1134)7686
 
2.0%
ValueCountFrequency (%)
0368750
97.3%
0.316227766231
 
0.1%
0.42163702141
 
< 0.1%
0.48304589155
 
< 0.1%
0.6324555329
 
< 0.1%
0.84327404271
 
< 0.1%
0.84327404271
 
< 0.1%
0.94868329814
 
< 0.1%
0.94868329812
 
< 0.1%
1.2649110649
 
< 0.1%
ValueCountFrequency (%)
202.38577031
< 0.1%
189.73665961
< 0.1%
157.79765521
< 0.1%
154.95160531
< 0.1%
142.30034751
< 0.1%
95.500785341
< 0.1%
86.959058051
< 0.1%
77.159574911
< 0.1%
76.855130681
< 0.1%
74.945980551
< 0.1%

dropped_frames_max
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct200
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.450719089
Minimum0
Maximum640
Zeros368604
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2022-10-05T03:41:34.001793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum640
Range640
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.670928366
Coefficient of variation (CV)6.666299793
Kurtosis210.1732448
Mean1.450719089
Median Absolute Deviation (MAD)0
Skewness9.770391129
Sum549853
Variance93.52685546
MonotonicityNot monotonic
2022-10-05T03:41:34.220521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0368604
97.3%
33561
 
0.1%
34532
 
0.1%
35427
 
0.1%
36339
 
0.1%
1265
 
0.1%
32254
 
0.1%
66234
 
0.1%
38230
 
0.1%
64225
 
0.1%
Other values (190)7350
 
1.9%
ValueCountFrequency (%)
0368604
97.3%
1265
 
0.1%
211
 
< 0.1%
314
 
< 0.1%
49
 
< 0.1%
52
 
< 0.1%
68
 
< 0.1%
77
 
< 0.1%
83
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
6401
< 0.1%
6001
< 0.1%
4991
< 0.1%
4901
< 0.1%
4551
< 0.1%
3021
< 0.1%
2681
< 0.1%
2441
< 0.1%
2371
< 0.1%
2331
< 0.1%

bitrate_mean
Real number (ℝ≥0)

HIGH CORRELATION

Distinct154342
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7516.585502
Minimum0
Maximum64913.5
Zeros290
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2022-10-05T03:41:34.705835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile511.4
Q12773.3
median6287.2
Q310187.2
95-th percentile19681.5
Maximum64913.5
Range64913.5
Interquartile range (IQR)7413.9

Descriptive statistics

Standard deviation6073.992189
Coefficient of variation (CV)0.8080786398
Kurtosis2.228573665
Mean7516.585502
Median Absolute Deviation (MAD)3769.6
Skewness1.226249417
Sum2848943754
Variance36893381.11
MonotonicityNot monotonic
2022-10-05T03:41:34.941367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
455307
 
0.1%
456.8301
 
0.1%
458.6293
 
0.1%
0290
 
0.1%
965249
 
0.1%
458.7241
 
0.1%
453.2222
 
0.1%
460.4217
 
0.1%
1151215
 
0.1%
456.9201
 
0.1%
Other values (154332)376485
99.3%
ValueCountFrequency (%)
0290
0.1%
3.31
 
< 0.1%
13.31
 
< 0.1%
15.61
 
< 0.1%
17.61
 
< 0.1%
17.91
 
< 0.1%
1811
 
< 0.1%
18.21
 
< 0.1%
18.94
 
< 0.1%
1918
 
< 0.1%
ValueCountFrequency (%)
64913.51
< 0.1%
62388.21
< 0.1%
61651.11
< 0.1%
61013.41
< 0.1%
60993.61
< 0.1%
60061.61
< 0.1%
60046.71
< 0.1%
59953.71
< 0.1%
59886.41
< 0.1%
59785.51
< 0.1%

bitrate_std
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct362263
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1603.487501
Minimum0
Maximum26908.5323
Zeros4511
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2022-10-05T03:41:35.191338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.03744159
Q1383.6835502
median1112.71001
Q32241.848801
95-th percentile4931.753242
Maximum26908.5323
Range26908.5323
Interquartile range (IQR)1858.16525

Descriptive statistics

Standard deviation1721.021623
Coefficient of variation (CV)1.073299057
Kurtosis10.02462319
Mean1603.487501
Median Absolute Deviation (MAD)851.5016854
Skewness2.309804912
Sum607755436.2
Variance2961915.428
MonotonicityNot monotonic
2022-10-05T03:41:35.444454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04511
 
1.2%
9.295160031264
 
0.1%
9.81155781164
 
< 0.1%
9.295160031163
 
< 0.1%
8.694826048134
 
< 0.1%
5.692099788121
 
< 0.1%
9.295160031113
 
< 0.1%
7.589466384110
 
< 0.1%
9.177871939107
 
< 0.1%
9.486832981103
 
< 0.1%
Other values (362253)373231
98.5%
ValueCountFrequency (%)
04511
1.2%
0.3162277661
 
< 0.1%
0.3162277661
 
< 0.1%
0.3162277661
 
< 0.1%
0.3162277662
 
< 0.1%
0.31622776613
 
< 0.1%
0.3162277661
 
< 0.1%
0.3162277663
 
< 0.1%
0.3162277668
 
< 0.1%
0.3162277666
 
< 0.1%
ValueCountFrequency (%)
26908.53231
< 0.1%
26833.091431
< 0.1%
26245.486261
< 0.1%
25877.933761
< 0.1%
25772.697551
< 0.1%
25220.174241
< 0.1%
25166.346781
< 0.1%
24716.565661
< 0.1%
24697.991161
< 0.1%
24348.737551
< 0.1%

Interactions

2022-10-05T03:41:25.247244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:40:55.108792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:40:58.913948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:02.537027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:06.174404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:09.958375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:13.899243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:17.658596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:21.323578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:25.641299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:40:55.529480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:40:59.318433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:02.898609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:06.574577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:10.341257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:14.258535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:18.026899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:21.728045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:26.039280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:40:55.941612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:40:59.690755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:03.328140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:07.025910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:10.760667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:14.649036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:18.413651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:22.116313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:26.444404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:40:56.345043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:00.075756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:03.733757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:07.457860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:11.578429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:15.008359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:18.792389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:22.554376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:26.901760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:40:56.742199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:00.514173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:04.162149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:07.876124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:11.942346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:15.643303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:19.190668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:22.972520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:27.304175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:40:57.198424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:00.892240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:04.543311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:08.311102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:12.308982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:16.076461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:19.606259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:23.391347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:27.723653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:40:57.623681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:01.308763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:04.947287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:08.724577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:12.705350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:16.493523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:19.981296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:23.807429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:28.207947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:40:58.063045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:01.725514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:05.364197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:09.146064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:13.077826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:16.875027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:20.404227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:24.388141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:28.689242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:40:58.499504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:02.125061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:05.760713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:09.559451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:13.539955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:17.278357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:20.853684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T03:41:24.813868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-10-05T03:41:35.663158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-05T03:41:35.992412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-05T03:41:36.304873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-05T03:41:36.633620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-05T03:41:29.047661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-05T03:41:29.672067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

fps_meanfps_stdrtt_meanrtt_stddropped_frames_meandropped_frames_stddropped_frames_maxbitrate_meanbitrate_std
024.40.51639891.16.7239210.00.00.0460.57.648529
128.62.06559199.715.9237770.00.00.0616.3155.414893
230.00.00000098.111.7987760.00.00.0565.29.077445
330.30.94868399.413.0145220.00.00.0573.828.350191
429.90.316228123.262.4763070.00.00.0585.859.458295
529.51.649916131.2114.2577980.00.00.0555.247.713963
624.30.48304698.316.4994950.00.00.0456.79.428680
724.50.971825141.9103.8144180.00.00.0511.4156.318905
830.00.000000107.518.7335110.00.00.0675.588.334277
930.00.471405108.210.9524220.00.00.01129.0989.477079

Last rows

fps_meanfps_stdrtt_meanrtt_stddropped_frames_meandropped_frames_stddropped_frames_maxbitrate_meanbitrate_std
37901140.00.00.00.00.00.00.03267.347.548922
37901240.00.00.00.00.00.00.03294.441.844155
37901340.00.00.00.00.00.00.03358.852.609251
37901440.00.00.00.00.00.00.03341.668.178198
37901540.00.00.00.00.00.00.03275.553.769570
37901640.00.00.00.00.00.00.03324.952.846633
37901740.00.00.00.00.00.00.03325.366.015234
37901840.00.00.00.00.00.00.03293.243.355892
37901940.00.00.00.00.00.00.03317.040.033319
37902040.00.00.00.00.00.00.03283.240.575855

Duplicate rows

Most frequently occurring

fps_meanfps_stdrtt_meanrtt_stddropped_frames_meandropped_frames_stddropped_frames_maxbitrate_meanbitrate_std# duplicates
49157.00.057.00.00.00.00.0965.00.0240
52560.00.086.00.00.00.00.01151.00.0202
49657.00.095.00.00.00.00.01004.00.0134
49557.00.094.00.00.00.00.0724.00.0125
31030.00.021.00.00.00.00.010151.00.063
7324.00.036.00.00.00.00.09178.00.041
26629.00.0216.00.00.00.00.02778.00.041
2220.00.00.00.00.00.00.00.00.037
23229.00.00.00.00.00.00.00.00.030
52260.00.057.00.00.00.00.01134.00.030